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My advisor has been asking me to generate plots I am uncomfortable with, since they overestimate the success of our approach, or make the data seem less noisy than it is. He says this is okay because the numbers in the paper don't matter as much as the high level message. What should I do?

We haven't fabricated data per se but some of the analyses are sketchy. For example in one plot we are comparing our approach to a baseline, but due to lack of data, any point that our approach fails on will also register as a failure when we apply the baseline method to it, meaning our approach will always look better.

Due to lack of data, there's no obvious "alternative plot"; the alternative would be not generating any plot, or generating a plot that underestimates the success of our approach (which he says is just as bad as overestimating its success). In some cases, our plot and another plot are equally defensible, but our plot looks a lot better so we kept it.

  • How were the data obtained? – adipro Oct 21 '16 at 19:00
  • first, where are you in your academic career? second, is your name going to go on the ruling paper? if you're an undergrad and your name will not appear anywhere just do it - but keep notes. If the numbers don't matter, then why include them? sounds bogus to me. – user61996 Oct 22 '16 at 23:29
  • It's unclear from the information you gave whether this constitute misconducts. In the first example it seems there is not much alternative, because of limitations in the data. The notion of look better is very ambiguous. Did you omit any data without reporting it? Did the plot procedure add to the data or picked an inappropriate scale to mislead the reader? – Three Diag Nov 10 '16 at 9:44
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Standard definitions of scientific misconduct consider omission of conflicting data an instance of data falsification, see, for example, the guidelines as applied by the BMJ, or Gupta 2013 on fraud and misconduct:

Falsifying data means altering the existing records. It is the deliberate distortion or omission of undesired data or results.

If your advisor asks you to deliberately produce a figure that downplays undesired results, he or she is asking you to falsify your data.

So, what should you do?

Speak again to your advisor. Tell him or her that you don't feel comfortable with the suggested plot, and explain that you feel obliged to report the data faithfully, including weaknesses of the analysis and conflicting observations.

If your advisor insists on using the falsifying plot, you should consider choosing a different advisor, because the present one may not teach you proper scientific conduct.

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I agree with Schmuddi, for the most part. However, if there is truly no 'alternative' plotting method (hard to believe) or way to represent your data (even harder to believe) and your advisor forces the issue then I think the best way to handle it is:

  1. Point blank ask them, preferably in an email, what they are asking you to do. This keeps a paper trail down the road.
  2. Make it extremely clear exactly what you did in the methods section.
  3. Refuse to have your name on the paper as an author if it comes to it. Or if the journal you're submitting to has a section for it, list the advisor as the one who designed the experiments, wrote the paper, or whatever is appropriate. This will make it clear what your contribution was - and who is accountable for what.

Again, as Schmuddi said, have an honest discussion with your advisor. If your department has one ask the ombudsman what you should do.

Hope this helps. Good luck.

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As far as I understood you aren't fabricating numbers,right?

I don't know which research field is yours, but when reporting on research results, it's necessary to discuss decisions of the experimental design that show how the results can be interpreted.

A useful framework for organizing this thinking is the collection of "validity" criteria: construct validity, internal validity, external validity, etc.

Maybe you just have to admit the limitations and describe what you did to minimize systematic errors.

Best,

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